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DTX Studio Clinic is a software program for the acquisition, management, transfer and analysis of dental and craniomaxillofacial image information, and can be used to provide design input for dental restorative solutions. It displays and enhances digital images from various sources to support the diagnostic process and treatment planning. It stores and provides these images within the system or across computer systems at different locations.

DTX Studio Clinic is a software interface for dental/medical practitioners used to analyze 2D and 3D imaging data, in a timely fashion, for the treatment of dental, craniomaxillofacial and related conditions. DTX Studio Clinic displays and processes imaging data from different devices (i.e. Intraoral and extraoral X-rays, (CB)CT scanners, intraoral scanners, intraoral and extraoral cameras).

Here's a breakdown of the acceptance criteria and study information for the DTX Studio Clinic device, based on the provided text:

Important Note: The provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device, not necessarily a comprehensive clinical study report. Therefore, some information requested (like specific sample sizes for test sets, the number and qualifications of experts for ground truth, adjudication methods, MRMC study effect sizes, and detailed information about training sets) is not explicitly stated in this document. The focus here is on software validation and verification.

Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical "acceptance criteria" in the format of a table with specific metrics (e.g., sensitivity, specificity, accuracy thresholds). Instead, the acceptance is based on demonstrating that the DTX Studio Clinic software performs its intended functions reliably and safely, analogous to the predicate and reference devices, as verified through software validation and engineering testing.

The "reported device performance" is primarily described through the software's functionality and its successful verification and validation.

Study Information

1. Sample sizes used for the test set and the data provenance:

   • Not explicitly stated in the provided text. The document mentions "verification testing" and "validation testing" but does not detail the specific sample sizes of images or patient cases used for these tests.
   • Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective. It focuses on the software's functionality and its comparison to predicate devices, rather than the performance on specific clinical datasets.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not explicitly stated in the provided text. The 510(k) summary primarily addresses software functionality verification and validation, not a diagnostic accuracy study involving expert ground truth.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not explicitly stated in the provided text.

4. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • No, an MRMC comparative effectiveness study was not done or reported. The document explicitly states: "No clinical data was used to support the decision of substantial equivalence." This type of study would involve clinical data and human readers.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • Yes, in spirit. The software validation and verification described are for the algorithm and software functionalities operating independently. While the device does not make autonomous diagnoses (it "supports the diagnostic process and treatment planning"), its individual features (like airway segmentation, image sorting, virtual tooth setup) are tested in a standalone manner in terms of their computational correctness and adherence to specifications. However, this is distinct from standalone clinical performance (e.g., an AI algorithm making a diagnosis without human input). The document focuses on the technical performance of the software.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • For the software verification and validation, the implicit "ground truth" would be the software's functional specifications and requirements. For features like measurements or segmentation, this would likely involve mathematical correctness checks or comparison to pre-defined anatomical models or manually delineated reference segmentations. It is not based on expert consensus, pathology, or outcomes data in a clinical diagnostic sense, as no clinical data was used for substantial equivalence.

7. The sample size for the training set:

   • Not explicitly stated in the provided text. The document describes a medical device software for image management and analysis, not a machine learning model that typically requires a large 'training set' in the deep learning sense. If any features (like the automatic image sorting or virtual tooth setup) utilize machine learning, the details of their training (including sample size) are not provided in this 510(k) summary.

8. How the ground truth for the training set was established:

   • Not explicitly stated in the provided text. As mentioned above, details about training sets are absent. If machine learning is involved in certain features, the ground truth would typically be established by expert annotation or curated datasets, but this is not detailed here.

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